Hiring junior talent in the age of AI

Prelude

I'm wrapping up my stint as Payflip's CTO at the end of this month, and decided to take this year's holiday season off, focusing on finding out what I want to do after this. That gives me a bit of downtime to reflect, write, and experiment. And in the spirit of that reflection & writing I'm coming back to some earlier drafts in my notebooks, like this one. It's something that stuck for me while acting as a hiring manager & recruiter at Payflip, rolling out AI in the company and meeting with others in the network:

Do 'juniors' still stand a chance to land a job in today's AI landscape?

Over the last half year, I've had talks with long-tenured senior engineers, junior candidates looking for a job, startup founders & people in tech leadership. One example that stayed in my mind was imec's CTO breakfast about all things hiring where this question got dropped by a first-time founder looking for advice on hiring: "Do I still hire juniors now that tools like Claude Code can produce a lot of good code and make my team more productive?"

I get that people resort to that question, but I believe it should always be countered with nuance. Let's dive in.

What hiring people looks like

I might sound snarky, but if you are diffing humans against AI, your (hiring) culture has likely always been about hiring resources over people. You’re looking at the amount of code produced as metric (indeed something agents do exceptionally well now), rather than someone’s curiosity, eagerness, self-awareness or collaborative skills. How young or inexperienced people translated those into hard skills, learned what production-ready, scalable software looked like by observing how you do that with a team. That is what hiring juniors has always been about.

Hiring for resources is a valid strategy of course (who am I to judge if it's a wrong one). But personally, whenever I was involved in hiring plans, I've believed in investing in people rather than being blinded by the instant gratification of hiring hands that could translate code into a result.

The new reality for future junior talent

What I really do believe, however, is that the bar to make the cut as a junior has immensely shifted. The scenario we've had for the last few years in the broader IT landscape of simply completing an education in IT and afterwards thinking that your baseline coding skill in TypeScript & React is still in high demand on the market is over.

Investment in juniors from companies' POV somehow got considered more expensive, risky and sometimes useless now that code became a commodity. I recently had lunch with a fresh junior developer, who was frustrated he isn't handwriting code anymore and is now expected to generate code like his other team members. Indeed I told him that time is over, and adapting to the team around you is still the job.

How to look at it now then, being a junior?

There's still high demand (the war for talent is realer than ever), but the quality bar has changed. Earn your spot in the interview: be the best AI-enabled engineer in your class, experiment and bring that into the interview. Show that your baseline coding skills are secondary to you being versed in agentic coding, apply AI as your learning system for best practices, showcase that you experiment with different tools & understand the practices that make for LLM-aided coding. Not just prompting your way through.

Interviewers will screen more strictly if they have to wonder how you’ll tag along with the rest of the team because their time investment has become more expensive. You'll need to show you can move swiftly, think bigger picture, and can be humble & aware of your blind spots, identifying those that are important at a company: architecture, quality attributes that scale code and teams. It will provide the immediate coaching ground companies that hire juniors will still help with, but they expect a new level of autonomy & skill development.

If you're a junior in IT right now who started an IT education in the last 3 years under the safety of job certainty, I won't lie: it will be tough as hell. Good enough won't make the cut anymore: differentiation and showing you're the cream of the crop is the only way.

AI stresses it, but the tale is as old as time

Great schools & educators have always realized that differentiation & high-quality bars are what they are developing people for under their roof.

A primary example is close to my heart: Look at the Devine course at Howest. They understood early on that, instead of educating in tech that is just 'in demand', they should invest in the tech of the future. I got educated in Node, React & serverless in school before it was a standard in Flanders' IT landscape. The quality bar was insanely high and made me sweat. Years later most other schools made that stack fixed in their curriculum and in the last five years, not knowing the principles of React got a frown when interviewing at any IT company.

They got that the job market of tomorrow doesn't require a coder, a designer or a researcher but the product builder profile and educated students in all those niches at the same time, resulting in graduates that could think in terms of product and execute in terms of modern-day quality.

Great educations will adapt this to the 'age of AI' and shift courses to agentic coding, will expect to see AI-enabled features in bachelor assignments, and will want to see AI leveraged as a tool to learn among their students while stressing them on being critical of just producing fast output over grasping the concept.

Adapting as a junior

More than ever you'll have to battle your way to the top of the class.

Putting my thoughts together as a tech leader, interviewer, hiring manager, ex-Deviner, and once a junior, this is what I think makes the difference:

• Hone your skills by embedding AI into your engineering workflow. Use it to learn how tech clicks, go deep, but then go hard & fast on automating the parts you know.
• Remain critical. Prioritize quality over output. Learn what great AI output looks like over achieving results fast.
• Experiment with these tools. There's a lot out there, but playing around with them will reveal the underlying patterns of how AI works, porting it across tools and becoming AI-fluent.
• Passion & experimentation stick and translate to seeing opportunity in the eyes of the interviewers. This was always the case as a nice-to-have in the eyes of companies hiring, but will now be expected.
• Show that your collaboration skills & autonomy shine over the code you can churn out. Code is a commodity. Collaboration traits grab attention.
• Use AI in your interviews with companies. Show don't tell.
• Pick the education that shines over the one that still only teaches you baseline coding.
• Be humble and introspective about what you're eager to learn but haven't mastered yet. Articulate that in interviews; bring up front what you need coaching in before the question is asked to show you have a roadmap seniors can coach you in.
• Think outside code: what shapes great products you love using? How does AI change our interactions with those products?